[CI] Upgrade vllm to newest commit (#3182)

### What this PR does / why we need it?
Upgrade vLLM to newest commit

- Fix the aclgraph doesn't work problem, caused by
24fab45d96
- Fix PoolerOutput import error, caused by
755ed7b05b
- Fix the aclgraph weight load error to keep the same with torchair fix.
4492e3a554

### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
All test should pass


- vLLM version: v0.10.2
- vLLM main:
52d0cb8458

---------

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

vllm_ascend/worker/model_runner_v1.py

@@ -64,11 +64,12 @@ from vllm.multimodal.inputs import MultiModalKwargsItem, PlaceholderRange
 from vllm.multimodal.utils import group_mm_kwargs_by_modality
 from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import SamplingType
-from vllm.sequence import IntermediateTensors, PoolerOutput
+from vllm.sequence import IntermediateTensors
 from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
 from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler,
                         LazyLoader, cdiv, get_dtype_size,
                         is_pin_memory_available)
+from vllm.utils.jsontree import json_map_leaves
 from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadataBuilder
 from vllm.v1.attention.backends.utils import (
     AttentionCGSupport, reorder_batch_to_split_decodes_and_prefills)
@@ -144,7 +145,9 @@ else:
 
 if not vllm_version_is("0.10.2"):
     from vllm.v1.kv_cache_interface import UniformTypeKVCacheSpecs
+    from vllm.v1.outputs import PoolerOutput
 else:
+    from vllm.sequence import PoolerOutput
     UniformTypeKVCacheSpecs = None
 
 
@@ -1806,18 +1809,30 @@ class NPUModelRunner(LoRAModelRunnerMixin):
                                               device=hidden_states.device)
         seq_lens_cpu = self.seq_lens_cpu[:self.input_batch.num_reqs]
 
-        # Pooling models D2H & synchronize occurs in pooler.py:build_output
-        raw_pooler_output = self.model.pooler(
-            hidden_states=hidden_states, pooling_metadata=pooling_metadata)
+        if vllm_version_is("0.10.2"):
+            # Pooling models D2H & synchronize occurs in pooler.py:build_output
+            raw_pooler_output = self.model.pooler(
+                hidden_states=hidden_states, pooling_metadata=pooling_metadata)
+        else:
+            model = cast(VllmModelForPooling, self.model)
+            raw_pooler_output = model.pooler(
+                hidden_states=hidden_states,
+                pooling_metadata=pooling_metadata,
+            )
+            raw_pooler_output = json_map_leaves(
+                lambda x: x.to("cpu", non_blocking=True),
+                raw_pooler_output,
+            )
+            torch.npu.synchronize()
 
         pooler_output: list[Optional[torch.Tensor]] = []
         for raw_output, seq_len, prompt_len in zip(
                 raw_pooler_output, seq_lens_cpu, pooling_metadata.prompt_lens):
-
-            if seq_len == prompt_len:
-                pooler_output.append(raw_output.data)
+            if vllm_version_is("0.10.2"):
+                output = raw_output.data if seq_len == prompt_len else None
             else:
-                pooler_output.append(None)
+                output = raw_output if seq_len == prompt_len else None
+            pooler_output.append(output)
 
         return ModelRunnerOutput(
             req_ids=self.input_batch.req_ids,
@@ -2582,7 +2597,10 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         for task in self.get_supported_pooling_tasks():
             # Run a full batch with each task to ensure none of them OOMs
             output = self._dummy_pooler_run_task(hidden_states, task)
-            output_size[task] = output.get_data_nbytes()
+            if vllm_version_is("0.10.2"):
+                output_size[task] = output.get_data_nbytes()
+            else:
+                output_size[task] = sum(o.nbytes for o in output)
             del output  # Allow GC
 
         max_task = max(output_size.items(), key=lambda x: x[1])[0]

vllm_ascend/worker/worker_v1.py

@@ -116,6 +116,12 @@ class NPUWorker(WorkerBase):
             # Buffers saved before sleep
             self._sleep_saved_buffers: dict[str, torch.Tensor] = {}
 
+        # FixMe: this is a patch to fix the issue cause by https://github.com/vllm-project/vllm/commit/de94289a98d7ec52a5ef02719e01a1db8b505170
+        from vllm.model_executor.layers.linear import \
+            WEIGHT_LOADER_V2_SUPPORTED
+        if "UnquantizedLinearMethod" in WEIGHT_LOADER_V2_SUPPORTED:
+            WEIGHT_LOADER_V2_SUPPORTED.remove("UnquantizedLinearMethod")
+
     def sleep(self, level: int = 1) -> None:
         if not sleep_mode_enabled():
             raise ValueError(